Shift left model execution in software delivery

ABSTRACT

Disclosed herein a system, method, and computer program product for providing proactive guidance to users in order to execute a shift left model in software delivery. As disclosed, a processor may receive an issue resolution request. The processor may further access an issue resolution request repository where the issue resolution request repository may include details related to prior issue resolution requests. The processor may subsequently classify the issue resolution request based on the details related to the prior issue resolution requests. Accordingly, the processor may identify a root cause for the issue resolution request.

BACKGROUND

The present disclosure relates generally to the field of applicationmanagement and support (AMS), and more specifically to providingproactive guidance to users in order to execute a shift left model insoftware delivery.

Shift left is a practice intended to find and prevent defects early inthe software delivery process. The idea is to improve quality by movingtasks to the left as early as possible in the lifecycle. Shift lefttesting means testing earlier in the software development process.

SUMMARY

Embodiments of the present disclosure include a method, computer programproduct, and system for providing proactive guidance to users in orderto execute a shift left model in software delivery. A processor mayreceive an issue resolution request. The processor may access an issueresolution request repository. The issue resolution request repositorymay include details related to prior issue resolution requests. Theprocessor may classify the issue resolution request based on the detailsrelated to the prior issue resolution requests. The processor mayidentify a root cause for the issue resolution request.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present disclosure are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 illustrates a block diagram of an example proactive shift leftguidance system, in accordance with aspects of the present disclosure.

FIG. 2A illustrates a flowchart of a high-level example method forproviding proactive guidance to users in order to execute a shift leftmodel in software delivery, in accordance with aspects of the presentdisclosure.

FIG. 2B illustrates a flowchart of a low-level example method forproviding proactive guidance to users in order to execute a shift leftmodel in software delivery, in accordance with aspects of the presentdisclosure.

FIG. 3A illustrates a cloud computing environment, in accordance withaspects of the present disclosure.

FIG. 3B illustrates abstraction model layers, in accordance with aspectsof the present disclosure.

FIG. 4 illustrates a high-level block diagram of an example computersystem that may be used in implementing one or more of the methods,tools, and modules, and any related functions, described herein, inaccordance with aspects of the present disclosure.

While the embodiments described herein are amenable to variousmodifications and alternative forms, specifics thereof have been shownby way of example in the drawings and will be described in detail. Itshould be understood, however, that the particular embodiments describedare not to be taken in a limiting sense. On the contrary, the intentionis to cover all modifications, equivalents, and alternatives fallingwithin the spirit and scope of the disclosure.

DETAILED DESCRIPTION

Aspects of the present disclosure relate generally to the field ofapplication management and support (AMS), and more specifically toproviding proactive guidance to users in order to execute a shift leftmodel in software delivery. While the present disclosure is notnecessarily limited to such applications, various aspects of thedisclosure may be appreciated through a discussion of various examplesusing this context.

The proposed solution discussed throughout this disclosure is anAI-enabled solution which utilizes the benefit of shift left that canmaximize any AMS and software development project. Shift left is apractice intended to find and prevent defects early in the softwaredelivery process. The idea is to improve quality by moving tasks to theleft as early as possible in the lifecycle. Shift left testing meanstesting earlier in the software development process.

Shift left should be a conscious effort to improve IT service deliveryand support rather than something designed to save resources (e.g.,capital, processing power, etc.) on a piecemeal basis. It is a strategythat is focused on a number of benefits, such as: speedier incident andservice request resolution, cost reductions, better use of scarcetechnical know-how and capabilities, delivering a betterend-user/customer experience, etc.

To achieve these benefits however, organizations/entities may need toappreciate that driving shift left change purely to save resources willmost likely cause more of a detriment than help. For instance, an ITself-service capability that is designed and delivered purely to saveresources will most likely not be used sufficiently to make a realdifference to IT support operations or to deliver the expected resourcebenefits.

As already mentioned, shift left can make a significant difference to ITservice management and IT support operations. In particular, in the unitcost (e.g., the cost per ticket/issue resolution request) of dealingwith end-user/user/customer issues. For instance, it has been shown thatthe cost of Level 0 support is less than 10% of Level 1 costs.

For further reference, it has been shown that incident prevention costs$0 USD, Level 0 (self-help) costs around $2 USD, Level 1 (service deskhelp) costs around $22 USD, desktop support help costs around $69 USD,Level 2 (IT support help) costs around $104 USD, and Level 3 (vendorsupport help) costs around $599 USD. Thus, the concept of shift leftmeans shifting issue help to the left, e.g., a lower level.

Accordingly, the more tickets/issue resolution requests that can beshifted to the left, the more inexpensive they become to resolve (orprovision against). For example, a $22 USD Level 1 “human” passwordreset versus a $2 USD Level 0 automated password reset.

It is noted that in the traditional software development model,requirements are kept on the left side of the plan, and delivery andtesting requirements are on the right side. The problem in such adevelopment model is that these practices cannot handle changingexpectations and requirements, which leads to negative outcomes such asincreased costs, increased time to market, and unexpected errors.Another issue to arise is that with any large application supportaccount, large amounts of tickets are received on a monthly basis andthe application support IT team members/resources are generally a fixedcount. Thus, shift left helps the application support organization tomanage the support tickets in an optimal manner; some of the tickets canbe solved by the business users while creating the ticket, and sometickets might need to be resolved by the Level 3 support team.Accordingly, the solution presented herein provides an appropriateguidance that will be provided to a business user for maximizing thebenefit of shifting left.

Before turning to the FIGS. it may be beneficial to discuss thenovelties of said solution:

While interacting with any business application by a (business) user,the proposed solution will predict any problem/issue the user might haveto face associated with the application, and accordingly the proposed(AI) solution will proactively create/generate an appropriate guidancefor the user so that the problem can be solved in a proactive manner bythe user without creating/generating any service ticket/issue resolutionrequest that will be forwarded to another level of help;

While creating/generating any service ticket by any user, the proposedsolution will analyze the ticket based on the description from the userand/or application behavior and accordingly the proposed solution willidentify an appropriate guidance for the user so that the ticket that isbeing created can be resolved immediately after the creation of theticket;

If any ticket is created, with the AI nature of the solution, if thecreated ticket cannot be resolved by the user on their own, then, theproposed solution will analyze the ticket and predict the appropriatestage of service delivery/support level where the ticket can beresolved;

Based on historical data/information/details of creation of previoustickets by different users, the proposed solution will predict ifappropriate user training is required so that the creation of the ticketcan be reduced, and accordingly the proposed solution will create anappropriate training (e.g., a guidance sequence that is a trainingsequence) to interact with the applications so that the tickets can bereduced;

The proposed solution will identify if multiple users are creatingsame/similar types of tickets, or having same/similar problems/issues,and accordingly, the proposed solution will create an appropriatecollaborative learning (guidance sequence) so that the identified userscan solve the problem/issue together;

The proposed solution will identify a current level of engagement of theuser(s), and determine/identify if providing a guidance is appropriateduring that time in order to solve the problem/issue (e.g., determine iffixing the issue at a particular time is worth disengaging the user fromtheir current task), and accordingly, the proposed solution willidentify appropriate timing when the problem/issue can be solved withappropriate guidance; and/or

The proposed solution can be indexed with a type of device (e.g., aservice module, etc.) and mode of access for solving the reported issuesand also identify the resources needed for it, and accordingly, will beable to switch views, supplement the user with additional material,and/or initiate/invoke a collaborative channel to handshake with Level 3support.

Referring now to FIG. 1 , illustrated is block diagram of an exampleproactive shift left guidance system 100, in accordance with aspects ofthe present disclosure. As depicted, the proactive shift left guidancesystem 100 includes a user 102, a user device 104, an interactionanalysis module 106, an issue resolution request 108, an issue predictor110 that includes an issue resolution request repository 112, aknowledge corpus 114, and a guidance (sequence) 116.

In some embodiments, the user 102 is interacting with an application(not depicted) via the user device 104. In some embodiments, theapplication may be partnered with the proactive shift left guidancesystem 100 (e.g., the application is subscribed to the proactive serviceof the system). The user 102 may begin to experience an issue/problemwith the application and reach out for support for this issue via asupport link within the application. The user then begins thecreation/generation of the issue resolution request 108.

In some embodiments, upon the creation/generation of the issueresolution request 108, the user 102 is automatically asked to opt-in toallowing the interaction analysis module 106 to follow theirinteractions with the application and/or with their interaction/inputassociated with the issue resolution request 108. In some embodiments,after opting-in, the user 102 may save their selection and if asubsequent issue arises, the interaction analysis module 106 mayautomatically begin following interactions.

In some embodiments, the interaction analysis module 106 begins toidentify a specific/particular interaction or keyword the user 102 isusing (e.g., the user 102 clicks multiple times on a link, the user 102has included the words “not loading” in their issue resolution request,etc.). Upon identification of the specific/particular interaction and/orkeyword, the interaction analysis module 106 begins to communicate withthe issue predictor 110.

The issue predictor 110 takes the information from the interactionanalysis module 106 and accesses either or both the issue resolutionrequest repository 112 and/or the knowledge corpus 114. As depicted, theissue resolution request repository 112 and the knowledge corpus 114 mayinteract with one another to find a common Nexus between theinteraction/keyword provided by the interaction analysis module 106 andother prior issue resolution requests (e.g., if the user 102 is clickingtoo fast, they may be causing a loading glitch, which was the case in aprior issue resolution request, etc.). In some embodiments, the priorissue resolution requests may be tagged with indicators or metadata(e.g., loading issue tag, multiple click tag, etc.) that help the issuepredictor 110 locate the same or similar issues that the user 102 isexperiencing. In some embodiments, the issue resolution requestrepository 112 may be incorporated with the knowledge corpus 114 (e.g.,the knowledge corpus 114 may be housed within the issue resolutionrequest repository 112).

In some embodiments, once the issue predictor 110 identifies a likelyroot cause for the issue with the application (e.g., as determined by asimilarity between the issue resolution request108/interactions/keywords/etc.) the issue predictor 110 generates theguidance 116 that may be provided to the user 102 via the user device104. In some embodiments, the guidance 116 may be any of: a guidedsequence for the user to try to resolve the issue (e.g., use command “r”to reload the page, then click the link once), a guided list of words topresent in the issue resolution request 108 that will help a differentlevel support operator (e.g., indicate that the page has been frozen for20 mins), an automatic issue resolution request to preload as the issueresolution request 108 (e.g., this requests mirrors exactly issueresolution request #XXYY from the issue resolution request repository112), etc.

In some embodiments, as depicted, if the user 102 does not opt-in to theinteraction analysis module 106 following their interactions, the user102 can finish the issue resolution request 108 and it can be used bythe issue predictor 110 to predict the issue and provide the guidance116.

Turning to a more in-depth example associated with the proactive shiftleft guidance system 100, the proactive shift left guidance system 100will have an AI-module (e.g., processor) to classify tickets (e.g.,issue resolution requests) created/generated by different users.

The proactive shift left guidance system 100 will historically trackticket details from a ticket repository (e.g., issue resolution requestrepository 112), and the ticket detail(s) will be a: ticket description,ticket resolution steps, who created the ticket, when the ticket was/iscreated, application name associated with the ticket/issue, resolvergroup (e.g., which level of support), etc.

In some embodiments, based on historically gathered ticket details, theproactive shift left guidance system 100 will classify the tickets,where the classification of the tickets can be based on resolver group,types of solutions applied, etc. Further, based on the ticket details,the proactive shift left guidance system 100 will identify a possibleroot cause of the tickets; generally root causes are provided while anyticket is resolved.

In some embodiments, based on historical information of differenttickets the proactive shift left guidance system 100 willcreate/generate the knowledge corpus 114 about the tickets. In someembodiments, while interacting with any application by a user, theproactive shift left guidance system 100 will also beanalyzing/following/tracking user interaction(s) with the application(using the interaction analysis module 106), and will track when theuser is creating any ticket.

Based on historical interactions with the application, and a ticketcreation at a later point of time, the proactive shift left guidancesystem 100 will predict (using the issue predictor 110) if any activityis problematic, and predict a reason for creating the ticket.

The proactive shift left guidance system 100 will create the knowledgecorpus 114 about the user 102's interaction behavior with theapplication which can be an indication that the user 102 is having aproblem with the application which has led to the creation of the ticket(e.g., issue resolution request 108). In some embodiments, based on thecreated knowledge corpus 114, the proactive shift left guidance system100 will predict what types of problems the user 102 is having and whattypes of ticket can be created by the user 102.

In some embodiments, based on the knowledge corpus 114, the proactiveshift left guidance system 100 will be able to identify types ofresolutions (e.g., guidance 116) and resolution steps (e.g., guidancesequences) for any classified ticket.

In some embodiments, while the user 102 is interacting with anyapplication, then the proactive shift left guidance system 100 willidentifying/analyze user 102 interaction behavior with the application,and generate an application log associated with the interactionbehavior. The proactive shift left guidance system 100 will predict,from the interaction behavior and/or application log, what types ofproblem the user 102 may be having and what types of tickets can becreated based on the interaction behavior and/or application log.

In some embodiments, the identified information from the interactionbehavior and/or application log, will be used to predict a possible rootcause of the problem, such as if the problem can be resolved withtraining to the user 102. In such an embodiment, the proactive shiftleft guidance system 100 will identify appropriate training and providethe training (e.g., guidance 116) to the user 102 to solve the problem.

In some embodiments, if the proactive shift left guidance system 100predicts any problem(s), then the proactive shift left guidance system100 will show/provide appropriate guidance (e.g., 116) to the user 102so that, without creating a ticket, the user 102 can (re)solve theproblem.

In some embodiments, the resolution can be provided with an AR/VRdevice, a text overlay, and/or audio information to the user 102 so thatthe problem can be resolved without creating a ticket. Based on theguidance 116 to the user 102, the proactive shift left guidance system100 can understand how the problem can be solved, and the user 102 doesnot have to create the ticket.

In some embodiments, while creating any ticket, the proactive shift leftguidance system 100 will read textual information in the tickets, andaccording guide the user 102 to show how the problem can be solved. Insome embodiments, the proactive shift left guidance system 100 willevaluate if the user 102 can be guided to solve the problem withoutcreating the ticket and if an appropriate guidance (e.g., 116) is notavailable then the proactive shift left guidance system 100 willautomatically and directly assign the ticket to an appropriate group(e.g., support level).

Referring now to FIG. 2A, illustrated is a flowchart of a high-levelexample method 200 for providing proactive guidance to users in order toexecute a shift left model in software delivery, in accordance withaspects of the present disclosure. In some embodiments, the method 200may be performed by a processor (e.g., of the proactive shift leftguidance system 100, etc.).

In some embodiments, the method 200 begins at operation 202, where theprocessor receives an issue resolution request (e.g., a ticket). In someembodiments, the method 200 proceeds to operation 204, where theprocessor accesses an issue resolution request repository. The issueresolution request repository may include details (e.g., tags associatedwith issues, classification type of the issues, etc.) relatedto/associated with prior issue resolution requests (e.g., where theprior issue resolution requests could be related to an assortment ofother respective users).

In some embodiments, the method 200 proceeds to operation 206, where theprocessor classifies the issue resolution request based on the detailsrelated to the prior issue resolution requests. In some embodiments, themethod 200 proceeds to operation 208, where the processor identifies aroot cause for the issue resolution request (e.g., the installation ofthe application was corrupted, the user is using the wrong tool, etc.).

In some embodiments, discussed below, there are one or more operationsof the method 200 not depicted for the sake of brevity and which arediscussed throughout this disclosure. Accordingly, in some embodiments,the processor may further incorporate the issue resolution request intothe issue resolution requests repository and generate a knowledge corpusin the issue resolution request repository. In some embodiments, theknowledge corpus may include predicted connections (e.g., a commonNexus) between the issue resolution request and the prior issueresolution requests (e.g., if classified as X then likely root cause isY and this X-classified issue could then relate to this other ticketthat is having this same X-classified issue, or it is known that issuesrelated to Y-root cause are known to have a Z-classified issue too,etc.).

In some embodiments, the processor may further identify a generation ofthe issue resolution request and analyze/follow user interactions withan application. The application may be associated with the issueresolution request. The processor may further predict that an issue withthe application will occur, based on the classifying of the issueresolution request and the analyzing of user interactions. The processormay generate an indication about the issue and provide the indication toa user (or users depending on the application).

In some embodiments, the indication is a guidance, and the processorfurther predicts a classification for the issue resolution request. Theclassification may be associated with a specific issue (e.g., 404 error,lag issues, etc.). The processor may identify a resolution for thespecific issue and provide the guidance to the suer to pre-empt thespecific issue (e.g., provide training to the user on how to use theapplication/fix the issue, how to fill in the issue resolution requestsuch that it is filtered to a correct level of support, etc.).

In some embodiments, the processor may further identify that theguidance pre-empted the specific issue and provide the user anopportunity to cancel the generation of the issue resolution request. Insome embodiments, if the processor identifies that the guidancepre-empted the specific issue (e.g., prevented the specific issue fromoccurring or resolved the specific issue) the processor mayautomatically stop the generation of the issue resolution request.

In some embodiments, the processor may identify that the guidance didnot pre-empt the specific issue and provide the user with an addendum(e.g., an add-on, an update, etc.) to the guidance. The addendum mayprovide concise terms to include in the issue resolution request (whichmay help filter the issue resolution request to a correct level ofsupport). In some embodiments, the addendum may provide an attritionstep or avenue for the user to attempt in order to resolve the specificissue.

Turning now to FIG. 2B, illustrated is a flowchart of a low-levelexample method 220 for providing proactive guidance to users in order toexecute a shift left model in software delivery, in accordance withaspects of the present disclosure. As depicted, in some embodiments, themethod 220 may have operations 222 and 224 performed by a processor. Theprocessor, at operation 222, may analyze user interaction(s) with anapplication and at operation 224, the processor may record the userinteraction(s) to an application log. The analysis and recordings atrespectively at operations 222 and 224 may then be used by the processorto identify, at operation 226, an issue while a user interacts with theapplication.

In some embodiments, after operation 226, the processor, at operation228, may associate the issue with a classification of issue resolutionrequests. In some embodiments, after operation 228, the processor, atoperation 236, may identify a root cause associated with the issue (ofthe issue resolution request). In some embodiments, before the method220 proceeds to operation 236, the processor may identify, at operation230, that generation of an issue resolution request has been initiated;this identification could be used in communication with operation 228 tofind a correct classification association.

In some embodiments, the processor may resolve, at operation 232, theissue resolution request and provide details (e.g., classifications,metadata, etc.) to a knowledge corpus that could help resolve subsequentissue resolution requests. In some embodiments, the processor, atoperation 234, may classify the issue resolution request. In someembodiments, after operation 234 and/or simultaneously with operation228, the processor may identify, at operation 236, the root cause.

In some embodiments, after operation 236, the processor may identify, atoperation 238, a guidance. In some embodiments, in identifying aguidance at operation 238, the processor may predict, at operation 240,the issue associated with the issue resolution request.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present disclosure are capable of being implementedin conjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of portion independence in that the consumergenerally has no control or knowledge over the exact portion of theprovided resources but may be able to specify portion at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

FIG. 3A, illustrated is a cloud computing environment 310 is depicted.As shown, cloud computing environment 310 includes one or more cloudcomputing nodes 300 with which local computing devices used by cloudconsumers, such as, for example, personal digital assistant (PDA) orcellular telephone 300A, desktop computer 300B, laptop computer 300C,and/or automobile computer system 300N may communicate. Nodes 300 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof.

This allows cloud computing environment 310 to offer infrastructure,platforms and/or software as services for which a cloud consumer doesnot need to maintain resources on a local computing device. It isunderstood that the types of computing devices 300A-N shown in FIG. 3Aare intended to be illustrative only and that computing nodes 300 andcloud computing environment 310 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

FIG. 3B, illustrated is a set of functional abstraction layers providedby cloud computing environment 310 (FIG. 3A) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3B are intended to be illustrative only and embodiments of thedisclosure are not limited thereto. As depicted below, the followinglayers and corresponding functions are provided.

Hardware and software layer 315 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 302;RISC (Reduced Instruction Set Computer) architecture based servers 304;servers 306; blade servers 308; storage devices 311; and networks andnetworking components 312. In some embodiments, software componentsinclude network application server software 314 and database software316.

Virtualization layer 320 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers322; virtual storage 324; virtual networks 326, including virtualprivate networks; virtual applications and operating systems 328; andvirtual clients 330.

In one example, management layer 340 may provide the functions describedbelow. Resource provisioning 342 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 344provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 346 provides access to the cloud computing environment forconsumers and system administrators. Service level management 348provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 350 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 360 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 362; software development and lifecycle management 364;virtual classroom education delivery 366; data analytics processing 368;transaction processing 370; and providing proactive guidance to users inorder to execute a shift left model in software delivery 372.

FIG. 4 , illustrated is a high-level block diagram of an examplecomputer system 401 that may be used in implementing one or more of themethods, tools, and modules, and any related functions, described herein(e.g., using one or more processor circuits or computer processors ofthe computer), in accordance with embodiments of the present disclosure.In some embodiments, the major components of the computer system 401 maycomprise one or more CPUs 402, a memory subsystem 404, a terminalinterface 412, a storage interface 416, an I/O (Input/Output) deviceinterface 414, and a network interface 418, all of which may becommunicatively coupled, directly or indirectly, for inter-componentcommunication via a memory bus 403, an I/O bus 408, and an I/O businterface unit 410.

The computer system 401 may contain one or more general-purposeprogrammable central processing units (CPUs) 402A, 402B, 402C, and 402D,herein generically referred to as the CPU 402. In some embodiments, thecomputer system 401 may contain multiple processors typical of arelatively large system; however, in other embodiments the computersystem 401 may alternatively be a single CPU system. Each CPU 402 mayexecute instructions stored in the memory subsystem 404 and may includeone or more levels of on-board cache.

System memory 404 may include computer system readable media in the formof volatile memory, such as random access memory (RAM) 422 or cachememory 424. Computer system 401 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 426 can be provided forreading from and writing to a non-removable, non-volatile magneticmedia, such as a “hard drive.” Although not shown, a magnetic disk drivefor reading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), or an optical disk drive for reading from orwriting to a removable, non-volatile optical disc such as a CD-ROM,DVD-ROM or other optical media can be provided. In addition, memory 404can include flash memory, e.g., a flash memory stick drive or a flashdrive. Memory devices can be connected to memory bus 403 by one or moredata media interfaces. The memory 404 may include at least one programproduct having a set (e.g., at least one) of program modules that areconfigured to carry out the functions of various embodiments.

One or more programs/utilities 428, each having at least one set ofprogram modules 430 may be stored in memory 404. The programs/utilities428 may include a hypervisor (also referred to as a virtual machinemonitor), one or more operating systems, one or more applicationprograms, other program modules, and program data. Each of the operatingsystems, one or more application programs, other program modules, andprogram data or some combination thereof, may include an implementationof a networking environment. Programs 428 and/or program modules 430generally perform the functions or methodologies of various embodiments.

Although the memory bus 403 is shown in FIG. 4 as a single bus structureproviding a direct communication path among the CPUs 402, the memorysubsystem 404, and the I/O bus interface 410, the memory bus 403 may, insome embodiments, include multiple different buses or communicationpaths, which may be arranged in any of various forms, such aspoint-to-point links in hierarchical, star or web configurations,multiple hierarchical buses, parallel and redundant paths, or any otherappropriate type of configuration. Furthermore, while the I/O businterface 410 and the I/O bus 408 are shown as single respective units,the computer system 401 may, in some embodiments, contain multiple I/Obus interface units 410, multiple I/O buses 408, or both. Further, whilemultiple I/O interface units are shown, which separate the I/O bus 408from various communications paths running to the various I/O devices, inother embodiments some or all of the I/O devices may be connecteddirectly to one or more system I/O buses.

In some embodiments, the computer system 401 may be a multi-usermainframe computer system, a single-user system, or a server computer orsimilar device that has little or no direct user interface, but receivesrequests from other computer systems (clients). Further, in someembodiments, the computer system 401 may be implemented as a desktopcomputer, portable computer, laptop or notebook computer, tabletcomputer, pocket computer, telephone, smartphone, network switches orrouters, or any other appropriate type of electronic device.

It is noted that FIG. 4 is intended to depict the representative majorcomponents of an exemplary computer system 401. In some embodiments,however, individual components may have greater or lesser complexitythan as represented in FIG. 4 , components other than or in addition tothose shown in FIG. 4 may be present, and the number, type, andconfiguration of such components may vary.

As discussed in more detail herein, it is contemplated that some or allof the operations of some of the embodiments of methods described hereinmay be performed in alternative orders or may not be performed at all;furthermore, multiple operations may occur at the same time or as aninternal part of a larger process.

The present disclosure may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Although the present disclosure has been described in terms of specificembodiments, it is anticipated that alterations and modification thereofwill become apparent to the skilled in the art. Therefore, it isintended that the following claims be interpreted as covering all suchalterations and modifications as fall within the true spirit and scopeof the disclosure.

What is claimed is:
 1. A system for providing proactive guidance tousers in order to execute a shift left model in software delivery, thesystem comprising: one or more processors, one or more computer-readablememories and one or more computer-readable storage media; programinstructions, stored on at least one of the one or more storage mediafor execution by at least one of the one or more processors via at leastone of the one or more memories, to receive an issue resolution request;program instructions, stored on at least one of the one or more storagemedia for execution by at least one of the one or more processors via atleast one of the one or more memories, to access an issue resolutionrequest repository, wherein the issue resolution request repositoryincludes details related to prior issue resolution requests; programinstructions, stored on at least one of the one or more storage mediafor execution by at least one of the one or more processors via at leastone of the one or more memories, to classify, based on the detailsrelated to the prior issue resolution requests, the issue resolutionrequest; program instructions, stored on at least one of the one or morestorage media for execution by at least one of the one or moreprocessors via at least one of the one or more memories, to identify aroot cause for the issue resolution request; program instructions,stored on at least one of the one or more storage media for execution byat least one of the one or more processors via at least one of the oneor more memories, to predict an issue associated with the root cause;program instructions, stored on at least one of the one or more storagemedia for execution by at least one of the one or more processors via atleast one of the one or more memories, to generate a guidance sequence,wherein the guidance sequence prevents the escalation of the issueresolution request; program instructions, stored on at least one of theone or more storage media for execution by at least one of the one ormore processors via at least one of the one or more memories, to resolvethe issue resolution request with the guidance sequence; programinstructions, stored on at least one of the one or more storage mediafor execution by at least one of the one or more processors via at leastone of the one or more memories, to incorporate the issue resolutionrequest into the issue resolution request repository; and programinstructions, stored on at least one of the one or more storage mediafor execution by at least one of the one or more processors via at leastone of the one or more memories, to generate a knowledge corpus in theissue resolution request repository, wherein the knowledge corpusincludes predicted connections between the issue resolution request andthe prior issue resolution requests.
 2. The system of claim 1, furthercomprising: program instructions, stored on at least one of the one ormore storage media for execution by at least one of the one or moreprocessors via at least one of the one or more memories, to identify ageneration of the issue resolution request; and program instructions,stored on at least one of the one or more storage media for execution byat least one of the one or more processors via at least one of the oneor more memories, to analyze user interactions with an application,wherein the application is associated with the issue resolution request.3. The system of claim 2, further comprising: program instructions,stored on at least one of the one or more storage media for execution byat least one of the one or more processors via at least one of the oneor more memories, to predict, based on the classifying of the issueresolution request and the analyzing of user interactions, that an issuewith the application will occur; program instructions, stored on atleast one of the one or more storage media for execution by at least oneof the one or more processors via at least one of the one or morememories, to generate an indication about the issue; and programinstructions, stored on at least one of the one or more storage mediafor execution by at least one of the one or more processors via at leastone of the one or more memories, to provide the indication to a user. 4.The system of claim 3, wherein the indication is the guidance sequence,and wherein the system further comprises: program instructions, storedon at least one of the one or more storage media for execution by atleast one of the one or more processors via at least one of the one ormore memories, to predict a classification for the issue resolutionrequest, wherein the classification is associated with a specific issue;program instructions, stored on at least one of the one or more storagemedia for execution by at least one of the one or more processors via atleast one of the one or more memories, to identify a resolution for thespecific issue; and program instructions, stored on at least one of theone or more storage media for execution by at least one of the one ormore processors via at least one of the one or more memories, to providethe guidance to the user to pre-empt the specific issue.
 5. The systemof claim 4, further comprising: program instructions, stored on at leastone of the one or more storage media for execution by at least one ofthe one or more processors via at least one of the one or more memories,to identify that the guidance pre-empted the specific issue; and programinstructions, stored on at least one of the one or more storage mediafor execution by at least one of the one or more processors via at leastone of the one or more memories, to provide the user an opportunity tocancel the generation of the issue resolution request.
 6. The system ofclaim 4, further comprising: program instructions, stored on at leastone of the one or more storage media for execution by at least one ofthe one or more processors via at least one of the one or more memories,to identify that the guidance did not pre-empt the specific issue; andprogram instructions, stored on at least one of the one or more storagemedia for execution by at least one of the one or more processors via atleast one of the one or more memories, to provide the user with anaddendum to the guidance, wherein the addendum provides concise terms toinclude in the issue resolution request.
 7. A computer-implementedmethod for providing proactive guidance to users in order to execute ashift left model in software delivery, the method comprising: receiving,by a processor, an issue resolution request; accessing an issueresolution request repository, wherein the issue resolution requestrepository includes details related to prior issue resolution requests;classifying, based on the details related to the prior issue resolutionrequests, the issue resolution request; identifying a root cause for theissue resolution request; predicting an issue associated with the rootcause; generating a guidance sequence, wherein the guidance sequenceprevents the escalation of the issue resolution request; resolving theissue resolution request with the guidance sequence; incorporating theissue resolution request into the issue resolution request repository;and generating a knowledge corpus in the issue resolution requestrepository, wherein the knowledge corpus includes predicted connectionsbetween the issue resolution request and the prior issue resolutionrequests.
 8. The computer-implemented method of claim 7, furthercomprising: identifying a generation of the issue resolution request;and analyzing user interactions with an application, wherein theapplication is associated with the issue resolution request.
 9. Thecomputer-implemented method of claim 8, further comprising: predicting,based on the classifying of the issue resolution request and theanalyzing of user interactions, that an issue with the application willoccur; generating an indication about the issue; and providing theindication to a user.
 10. The computer-implemented method of claim 9,wherein the indication is the guidance sequence, and wherein the methodfurther comprises: predicting a classification for the issue resolutionrequest, wherein the classification is associated with a specific issue;identifying a resolution for the specific issue; and providing theguidance to the user to pre-empt the specific issue.
 11. Thecomputer-implemented method of claim 10, further comprising: identifyingthat the guidance pre-empted the specific issue; and providing the useran opportunity to cancel the generation of the issue resolution request.12. The computer-implemented method of claim 10, further comprising:identifying that the guidance did not pre-empt the specific issue; andproviding the user with an addendum to the guidance, wherein theaddendum provides concise terms to include in the issue resolutionrequest.
 13. A computer program product for providing proactive guidanceto users in order to execute a shift left model in software deliverycomprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya processor to cause the processor to perform operations, the operationscomprising: receiving an issue resolution request; accessing an issueresolution request repository, wherein the issue resolution requestrepository includes details related to prior issue resolution requests;classifying, based on the details related to the prior issue resolutionrequests, the issue resolution request; identifying a root cause for theissue resolution request; predicting an issue associated with the rootcause; generating a guidance sequence, wherein the guidance sequenceprevents the escalation of the issue resolution request; resolving theissue resolution request with the guidance sequence; incorporating theissue resolution request into the issue resolution request repository;and generating a knowledge corpus in the issue resolution requestrepository, wherein the knowledge corpus includes predicted connectionsbetween the issue resolution request and the prior issue resolutionrequests.
 14. The computer program product of claim 13, wherein theprocessor is further configured to perform operations comprising:identifying a generation of the issue resolution request; and analyzinguser interactions with an application, wherein the application isassociated with the issue resolution request.
 15. The computer programproduct of claim 14, wherein the processor is further configured toperform operations comprising: predicting, based on the classifying ofthe issue resolution request and the analyzing of user interactions,that an issue with the application will occur; generating an indicationabout the issue; and providing the indication to a user.
 16. Thecomputer program product of claim 15, wherein the indication is theguidance sequence, and wherein the processor is further configured toperform operations comprising: predicting a classification for the issueresolution request, wherein the classification is associated with aspecific issue; identifying a resolution for the specific issue; andproviding the guidance to the user to pre-empt the specific issue. 17.The computer program product of claim 16, wherein the processor isfurther configured to perform operations comprising: identifying thatthe guidance pre-empted the specific issue; and providing the user anopportunity to cancel the generation of the issue resolution request.